Effects of Naturally Occurring Mutations in Bovine Leukemia Virus 5′-LTR and Tax Gene on Viral Transcriptional Activity
Bibliographic record
Abstract
Bovine leukemia virus (BLV) is a deltaretrovirus infecting bovine B cells and causing enzootic bovine leucosis (EBL). The long terminal repeat (LTR) plays an indispensable role in viral gene expression. The BLV Tax protein acts as the main transactivator of LTR-driven transcription of BLV viral genes. The aim of this study was to analyze mutations in the BLV LTR region and tax gene to determine their association with transcriptional activity. LTRs were obtained from one hundred and six BLV isolates and analyzed for their genetic variability. Fifteen variants were selected and characterized based on mutations in LTR regulatory elements, and further used for in vitro transcription assays. Reporter vectors containing the luciferase gene under the control of each variant BLV promoter sequence, in addition to variant Tax expression vectors, were constructed. Both types of plasmids were used for cotransfection of HeLa cells and the level of luciferase activity was measured as a proxy of transcriptional activity. Marked differences in LTR promoter activity and Tax transactivation activity were observed amongst BLV variants. These results demonstrate that mutations in both the BLV LTR and tax gene can affect the promoter activity, which may have important consequences on proviral load, viral fitness, and transmissibility in BLV-infected cattle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".